It is important to stress that this work aims to determine whether a given face sample belongs to any individual registered in the gallery set, and not at retrieving its genuine identity.
In that direction, our proposal is directly comparable to works in the literature [1]}.
Our main hypothesis is that Siamese Neural Networks (SNNs) can be effectively employed in open-set recognition.
A SNN is composed of two independent sub-networks with the same architecture and shared weights with their outputs joined at the end using a distance metric [2]}.
Each independent sub-network has its own input, and the whole network outputs a numeric distance value, indicating the similarity between the two inputs. Although the SNN performs face verification, the proposed architecture uses it to compute scores and perform open-set recognition.
Fig. REF  illustrates the process.
